Machine learning for power grid
First Claim
1. A machine learning system for determining propensity to failure metrics of like components within an electrical grid comprising:
- (a) a raw data assembly to provide raw data representative of the like components within the electrical grid;
(b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques;
(c) a database, operatively coupled to the data processor, to store the more uniform data;
(d) a machine learning engine, operatively coupled to the database, to provide;
(i) a ranking of the collection of propensity to failure metrics for the like components, and(ii) absolute value of the propensity to failure metrics for the like components;
(e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and
(f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid.
3 Assignments
0 Petitions
Accused Products
Abstract
A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid.
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Citations
22 Claims
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1. A machine learning system for determining propensity to failure metrics of like components within an electrical grid comprising:
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(a) a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide; (i) a ranking of the collection of propensity to failure metrics for the like components, and (ii) absolute value of the propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 21, 22)
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11. A method for determining propensity to failure metrics of like components within an electrical grid via machine learning comprising:
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(a) providing a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) processing the raw data to convert the raw data to more uniform data via one or more data processing techniques; (c) storing the more uniform data in a database; (d) transmitting the more uniform data to a machine learning engine to provide a collection of propensity to failure metrics for the like components; (e) evaluating the collection of propensity to failure metrics in an evaluation engine to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; (f) ranking the collection of filtered propensity to failure metrics obtained from the evaluation engine and displaying the ranking on a decision support application; and (g) determining an absolute value of the propensity to failure metrics for the like components. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification